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  • 1
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2017-10, Vol.55 (10), p.5653-5665
    Description: Scene classification from remote sensing images provides new possibilities for potential application of high spatial resolution imagery. How to efficiently implement scene recognition from high spatial resolution imagery remains a significant challenge in the remote sensing domain. Recently, convolutional neural networks (CNN) have attracted tremendous attention because of their excellent performance in different fields. However, most works focus on fully training a new deep CNN model for the target problems without considering the limited data and time-consuming issues. To alleviate the aforementioned drawbacks, some works have attempted to use the pretrained CNN models as feature extractors to build a feature representation of scene images for classification and achieved successful applications including remote sensing scene classification. However, existing works pay little attention to exploring the benefits of multilayer features for improving the scene classification in different aspects. As a matter of fact, the information hidden in different layers has great potential for improving feature discrimination capacity. Therefore, this paper presents a fusion strategy for integrating multilayer features of a pretrained CNN model for scene classification. Specifically, the pretrained CNN model is used as a feature extractor to extract deep features of different convolutional and fully connected layers; then, a multiscale improved Fisher kernel coding method is proposed to build a mid-level feature representation of convolutional deep features. Finally, the mid-level features extracted from convolutional layers and the features of fully connected layers are fused by a principal component analysis/spectral regression kernel discriminant analysis method for classification. For validation and comparison purposes, the proposed approach is evaluated via experiments with two challenging high-resolution remote sensing data sets, and shows the competitive performance compared with fully trained CNN models, fine-tuning CNN models, and other related works.
    Subject(s): Training ; Convolutional codes ; Visualization ; feature fusion ; Semantics ; spectral regression kernel discriminant analysis (SRKDA) ; Feature extraction ; Convolutional neural networks (CNN) ; Data mining ; scene classification ; Remote sensing ; improved Fisher kernel
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 2
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2015-05, Vol.53 (5), p.2532-2546
    Description: In this paper, we propose a new spectral-spatial classification strategy to enhance the classification performances obtained on hyperspectral images by integrating rotation forests and Markov random fields (MRFs). First, rotation forests are performed to obtain the class probabilities based on spectral information. Rotation forests create diverse base learners using feature extraction and subset features. The feature set is randomly divided into several disjoint subsets; then, feature extraction is performed separately on each subset, and a new set of linear extracted features is obtained. The base learner is trained with this set. An ensemble of classifiers is constructed by repeating these steps several times. The weak classifier of hyperspectral data, classification and regression tree (CART), is selected as the base classifier because it is unstable, fast, and sensitive to rotations of the axes. In this case, small changes in the training data of CART lead to a large change in the results, generating high diversity within the ensemble. Four feature extraction methods, including principal component analysis (PCA), neighborhood preserving embedding (NPE), linear local tangent space alignment (LLTSA), and linearity preserving projection (LPP), are used in rotation forests. Second, spatial contextual information, which is modeled by MRF prior, is used to refine the classification results obtained from the rotation forests by solving a maximum a posteriori problem using the α-expansion graph cuts optimization method. Experimental results, conducted on three hyperspectral data with different resolutions and different contexts, reveal that rotation forest ensembles are competitive with other strong supervised classification methods, such as support vector machines. Rotation forests with local feature extraction methods, including NPE, LLTSA, and LPP, can lead to higher classification accuracies than that achieved by PCA. With the help of MRF, the proposed algorithms can improve the classification accuracies significantly, confirming the importance of spatial contextual information in hyperspectral spectral-spatial classification.
    Subject(s): Training ; Accuracy ; Training data ; hyperspectral image classification ; Markov random fields (MRFs) ; Feature extraction ; rotation forests ; Hyperspectral imaging ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 3
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2016-03, Vol.54 (3), p.1519-1531
    Description: With different principles, support vector machines (SVMs) and multiple classifier systems (MCSs) have shown excellent performances for classifying hyperspectral remote sensing images. In order to further improve the performance, we propose a novel ensemble approach, namely, rotation-based SVM (RoSVM), which combines SVMs and MCSs together. The basic idea of RoSVM is to generate diverse SVM classification results using random feature selection and data transformation, which can enhance both individual accuracy and diversity within the ensemble simultaneously. Two simple data transformation methods, i.e., principal component analysis and random projection, are introduced into RoSVM. An empirical study on three hyperspectral data sets demonstrates that the proposed RoSVM ensemble method outperforms the single SVM and random subspace SVM. The impacts of the parameters on the overall accuracy of RoSVM (different training sets, ensemble sizes, and numbers of features in the subset) are also investigated in this paper.
    Subject(s): Support vector machines ; Training ; hyperspectral remote sensing image ; Accuracy ; multiple classifier systems (MCSs) ; Classification ; Hyperspectral imaging ; Principal component analysis ; rotation-based ensemble ; support vector machines (SVMs) ; Learning ; Usage ; Analysis ; Principal components analysis ; Regression analysis ; Research ; Remote sensing ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 4
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2019-06, Vol.57 (6), p.3813-3826
    Description: Recently, deep learning (DL) is of great interest in hyperspectral image (HSI) classification. Although many effective frameworks exist in the literature, the generally limited availability of training samples poses great challenges in applying DL to HSI classification. In this paper, we present a novel DL framework, namely, semisupervised stacked autoencoders (Semi-SAEs) with cotraining, for HSI classification. First, two SAEs are pretrained based on the hyperspectral features and the spatial features, respectively. Second, fine-tuning is alternatively conducted for the two SAEs in a semisupervised cotraining fashion, where the initial training set is enlarged by designing an effective region growing method. Finally, the classification probabilities obtained by the two SAEs are fused using a Markov random field model solved by iterated conditional modes. Experimental results based on three popular hyperspectral data sets demonstrate that the proposed method outperforms other state-of-the-art DL methods.
    Subject(s): Training ; Deep learning ; stacked autoencoders (SAEs) ; Feature extraction ; Markov random field (MRF) ; semisupervised cotraining ; Data mining ; Deep learning (DL) ; Hyperspectral imaging ; hyperspectral image (HSI) classification
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 5
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2019-02, Vol.57 (2), p.1230-1241
    Description: Spatial information has widely been used in hyperspectral image (HSI) classification to improve classification accuracy. However, the structural information may not be fully explored when using spatial information, this paper proposes the joint collaborative representation classification with correlation matrix (CRC-CM) for HSI by using spatial correlation features in patches, which could keep the local intrinsic structure in band images. Considering spatial heterogeneity in a patch, local correlation matrices of a target neighborhood patch and training neighborhood patch are improved by a binary weight matrix and shape-adaptive neighborhood. To explore nonlinear nature of spatial features, corresponding kernel CRC-CM is also proposed. To evaluate the effectiveness of the proposed methods, three real HSIs with different degree of heterogeneity are used. The experimental results show that the proposed spatial correlation features outperform the original spectral feature and other spatial features which widely used in HSI classifiers.
    Subject(s): Training ; Hyperspectral image (HSI) classification ; Correlation ; kernel collaborative representation (CR) ; Collaboration ; spatial correlation feature ; Kernel ; Testing ; Hyperspectral imaging
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 6
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2015-01, Vol.53 (1), p.244-260
    Description: The new generation of satellite hyperspectral (HS) sensors can acquire very detailed spectral information directly related to land surface materials. Thus, when multitemporal images are considered, they allow us to detect many potential changes in land covers. This paper addresses the change-detection (CD) problem in multitemporal HS remote sensing images, analyzing the complexity of this task. A novel hierarchical CD approach is proposed, which is aimed at identifying all the possible change classes present between the considered images. In greater detail, in order to formalize the CD problem in HS images, an analysis of the concept of "change" is given from the perspective of pixel spectral behaviors. The proposed novel hierarchical scheme is developed by considering spectral change information to identify the change classes having discriminable spectral behaviors. Due to the fact that, in real applications, reference samples are often not available, the proposed approach is designed in an unsupervised way. Experimental results obtained on both simulated and real multitemporal HS images demonstrate the effectiveness of the proposed CD method.
    Subject(s): Image sensors ; Satellites ; Image resolution ; Change detection (CD) ; hyperspectral (HS) images ; hierarchical analysis ; multiple changes ; remote sensing ; Sensors ; multitemporal analysis ; Hyperspectral imaging ; Environmental aspects ; Technology application ; Usage ; Artificial satellites ; Land cover
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 7
    Language: English
    In: IEEE geoscience and remote sensing letters, 2014-01, Vol.11 (1), p.239-243
    Description: In this letter, an ensemble learning approach, Rotation Forest, has been applied to hyperspectral remote sensing image classification for the first time. The framework of Rotation Forest is to project the original data into a new feature space using transformation methods for each base classifier (decision tree), then the base classifier can train in different new spaces for the purpose of encouraging both individual accuracy and diversity within the ensemble simultaneously. Principal component analysis (PCA), maximum noise fraction, independent component analysis, and local Fisher discriminant analysis are introduced as feature transformation algorithms in the original Rotation Forest. The performance of Rotation Forest was evaluated based on several criteria: different data sets, sensitivity to the number of training samples, ensemble size and the number of features in a subset. Experimental results revealed that Rotation Forest, especially with PCA transformation, could produce more accurate results than bagging, AdaBoost, and Random Forest. They indicate that Rotation Forests are promising approaches for generating classifier ensemble of hyperspectral remote sensing.
    Subject(s): Training ; ensemble learning ; hyperspectral remote sensing image ; decision tree ; Rotation Forest ; Forestry ; Classification ; Bagging ; Principal component analysis ; Hyperspectral imaging ; Engineering Sciences ; Signal and Image processing
    ISSN: 1545-598X
    E-ISSN: 1558-0571
    Source: IEEE Electronic Library (IEL)
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  • 8
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2016-05, Vol.54 (5), p.2733-2748
    Description: This paper presents a novel multitemporal spectral unmixing (MSU) approach to address the challenging multiple-change detection problem in bitemporal hyperspectral (HS) images. Differently from the state-of-the-art methods that are mainly designed at a pixel level, the proposed technique investigates the spectral-temporal variations at a subpixel level. The considered change detection (CD) problem is analyzed in a multitemporal domain, where a bitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted according to an automatic and unsupervised technique. Then, a change analysis strategy is designed to distinguish the change and no-change MT-EMs. An endmember-grouping scheme is applied to the changed MT-EMs to detect the unique change classes. Finally, the considered multiple-change detection problem is solved by analyzing the abundances of the change and no-change classes and their contribution to each pixel. The proposed approach has been validated on both simulated and real multitemporal HS data sets presenting multiple changes. Experimental results confirmed the effectiveness of the proposed method.
    Subject(s): Correlation ; Change detection (CD) ; land-cover transitions ; spectral unmixing ; multitemporal images ; remote sensing ; Indexes ; Satellites ; unsupervised analysis ; hyperspectral (HS) images ; Mixture models ; multiple changes ; Sensors ; Hyperspectral imaging
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 9
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2018-09, Vol.56 (9), p.5343-5356
    Description: Multiple types of features, e.g., spectral, filtering, texture, and shape features, are helpful for hyperspectral image (HSI) classification tasks. Combining multiple features can describe the characteristics of pixels from different perspectives, and always results in better classification performance. Recently, multifeature combination learning has been widely employed to the multitask-learning-based representation-based model to obtain a multifeature representation vector. However, the linear sparse representation-based classifier (SRC) cannot handle the HSI with highly nonlinear distribution, and kernel sparse representation-based classifier (KSRC) can remedy the drawback of linear SRC. By adopting nonlinear mapping, the samples in kernel space are often of high or even infinite dimensionality. In this paper, we integrate kernel principal component analysis into multifeature-based KSRC and propose a novel multiple feature kernel sparse representation-based classifier (namely, MFKSRC) for hyperspectral imagery. More specifically, spatial features, Gabor textures, local binary patterns, and difference morphological profiles are adopted and then each kind of feature is transformed nonlinearly into a new low-dimensional kernel space. The proposed framework can handle data with nonlinear distribution and add a dimensionality reduction stage in kernel space before optimizing the corresponding cost function. Experimental results on different HSIs demonstrate that the proposed MFKSRC algorithm outperforms the state-of-the-art classifiers.
    Subject(s): Hyperspectral image (HSI) classification ; Dictionaries ; Shape ; multiple feature learning ; Feature extraction ; kernel principal component analysis (KPCA) ; multitask learning ; Kernel ; Task analysis ; Hyperspectral imaging ; sparse representation ; Engineering Sciences ; Signal and Image processing
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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  • 10
    Language: English
    In: IEEE transactions on geoscience and remote sensing, 2015-11, Vol.53 (11), p.6114-6133
    Description: Sparse graph embedding (SGE) is a promising technique useful for the nonlinear feature extraction (FE) of hyperspectral images (HSIs). However, such images exhibit spatial variability and spectral multimodality, presenting challenges to existing FE methods, including SGE. To address this issue, this paper presents two novel SGE methods for HSI classification. One method, which is termed simultaneous SGE (SSGE), is designed to consider the spatial variability of spectral signatures by using a simultaneous sparse representation (SSR) model integrated with a shape-adaptive neighborhood building approach. In addition, a sparse graph is constructed via matrix computation based on sparse codes. Then, low-dimensional features are produced by employing linear graph embedding (LGE) based on the constructed sparse graph. The other method, which is termed simultaneous sparse multimanifold learning (SSMML), is proposed to handle the multimodality of an HSI. In SSMML, multiple views are generated to represent different modalities. Then, multiview-oriented submanifolds are produced by adopting SSGE, and they are further integrated via coregularization. SSGE is capable of modeling both local and global data structures. Furthermore, SSMML serves as a prototype that can model multimodal data structures. The proposed methods are evaluated by using sparse multinomial logistic regression for HSI classification. Experimental results with two popular hyperspectral data sets validate the good performance of the two methods in producing more representative low-dimensional features and yielding superior classification results compared with other related approaches.
    Subject(s): hyperspectral image (HSI) ; simultaneous sparse representation (SSR) ; Computational modeling ; Classification ; Data structures ; Feature extraction ; Iron ; linear graph embedding (LGE) ; sparse graph embedding (SGE) ; Sparse matrices ; Hyperspectral imaging ; multimanifold learning ; Usage ; Computer-generated environments ; Computer simulation ; Analysis ; Graph theory ; Matrices ; Regression analysis
    ISSN: 0196-2892
    E-ISSN: 1558-0644
    Source: IEEE Electronic Library (IEL)
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